import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import PoolFormerImageProcessor




class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=7 ,  lowerCAmelCase__=3 ,  lowerCAmelCase__=30 ,  lowerCAmelCase__=4_00 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=None ,  lowerCAmelCase__=0.9 ,  lowerCAmelCase__=None ,  lowerCAmelCase__=True ,  lowerCAmelCase__=[0.5, 0.5, 0.5] ,  lowerCAmelCase__=[0.5, 0.5, 0.5] ,  )	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				size if size is not None else {'''shortest_edge''': 30}
							__lowercase					     =				crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
							__lowercase					     =				parent
							__lowercase					     =				batch_size
							__lowercase					     =				num_channels
							__lowercase					     =				min_resolution
							__lowercase					     =				max_resolution
							__lowercase					     =				do_resize_and_center_crop
							__lowercase					     =				size
							__lowercase					     =				crop_pct
							__lowercase					     =				crop_size
							__lowercase					     =				do_normalize
							__lowercase					     =				image_mean
							__lowercase					     =				image_std





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							return {
							    "size": self.size,
							    "do_resize_and_center_crop": self.do_resize_and_center_crop,
							    "crop_pct": self.crop_pct,
							    "crop_size": self.crop_size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							}




@require_torch
@require_vision
class   _UpperCamelCase (		_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Tuple     					=		PoolFormerImageProcessor if is_vision_available() else None





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							__lowercase					     =				PoolFormerImageProcessingTester(self		)





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''do_resize_and_center_crop'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''size'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''crop_pct'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''do_normalize'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''image_mean'''		)		)
							self.assertTrue(hasattr(lowerCAmelCase__ ,  '''image_std'''		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class.from_dict(self.image_processor_dict		)
							self.assertEqual(image_processor.size ,  {'''shortest_edge''': 30}		)
							self.assertEqual(image_processor.crop_size ,  {'''height''': 30, '''width''': 30}		)

							__lowercase					     =				self.image_processing_class.from_dict(self.image_processor_dict ,  size=42 ,  crop_size=84		)
							self.assertEqual(image_processor.size ,  {'''shortest_edge''': 42}		)
							self.assertEqual(image_processor.crop_size ,  {'''height''': 84, '''width''': 84}		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							pass





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							__lowercase					     =				prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  Image.Image		)

							# Test not batched input
							__lowercase					     =				image_processing(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values

							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random numpy tensors
							__lowercase					     =				prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase__ ,  numpify=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  np.ndarray		)

							# Test not batched input
							__lowercase					     =				image_processing(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							__lowercase					     =				prepare_image_inputs(self.image_processor_tester ,  equal_resolution=lowerCAmelCase__ ,  torchify=lowerCAmelCase__		)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__ ,  torch.Tensor		)

							# Test not batched input
							__lowercase					     =				image_processing(image_inputs[0] ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )

							# Test batched
							__lowercase					     =				image_processing(lowerCAmelCase__ ,  return_tensors='''pt'''		).pixel_values
							self.assertEqual(
							    encoded_images.shape ,  (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.crop_size['''height'''],
							        self.image_processor_tester.crop_size['''width'''],
							    ) ,  )
import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
		print("""Googling.....""")
		__a    :	Tuple         =    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
		__a    :	List[str]         =    requests.get(url, headers={"""UserAgent""": UserAgent().random})
		# res.raise_for_status()
		with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
				for data in res.iter_content(1_0_0_0_0):
						out_file.write(data)
		__a    :	Dict         =    BeautifulSoup(res.text, """html.parser""")
		__a    :	Any         =    list(soup.select(""".eZt8xd"""))[:5]

		print(len(links))
		for link in links:
				if link.text == "Maps":
						webbrowser.open(link.get("""href"""))
				else:
						webbrowser.open(F'''https://google.com{link.get("href")}''')